The presence of sea clutter in marine radar signals is sometimes not desired. So, efficient radar signal processing techniques are needed to reduce it. In this way, nonlinear signal processing techniques based on neural networks (NNs) are used in the proposed clutter reduction system. The developed experiments show promising results characterized by different subjective (visual analysis of the processed radar images) and objective (clutter reduction, target enhancement and signal-to-clutter ratio improvement) criteria. Moreover, a deep study of the NN structure is done, where the low computational cost and the high processing speed of the proposed NN structure are emphasized.

This paper proposes a novel algorithm for retrieving the ocean wind vector from marine radar image sequences in real time. It is presented as an alternative to mitigate anemometer problems, such as blockage, shadowing, and turbulence. Since wind modifies the sea surface, the proposed algorithm is based on the dependence of the sea surface backscatter on wind direction and speed. This algorithm retrieves the wind vector using radar measurements in the range of 200-1500 m. Wind directions are retrieved from radar images integrated over time and smoothed (averaged) in space by searching for the maximum radar cross section in azimuth as the radar cross section is largest for upwind directions. Wind speeds are retrieved by an empirical third-order polynomial geophysical model function (GMF), which depends on the range distance in the upwind direction to a preselected intensity level and the intensity level. This GMF is approximated from a dataset of collocated in situ wind speed and radar measurements (;31 000 measurements, ;56 h). The algorithm is validated utilizing wind and radar measurements acquired on the Research Platform (R/P) FLIP (for Floating Instrumentation Platform) during the 13-day Office of Naval Research experiment on HighResolution Air-Sea Interaction (HiRes) in June 2010. Wind speeds ranged from 4 to 22 m s 21 . Once the proposed algorithm is tuned, standard deviations and biases of 148 and 218 for wind directions and of 0.8 and 20.1 m s 21 for wind speeds are observed, respectively. Additional studies of uncertainty and error of the retrieved wind speed are also reported.

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